# [mlpack-svn] r13039 - mlpack/trunk/doc/tutorials/emst

fastlab-svn at coffeetalk-1.cc.gatech.edu fastlab-svn at coffeetalk-1.cc.gatech.edu
Tue Jun 12 20:24:27 EDT 2012

Author: rcurtin
Date: 2012-06-12 20:24:27 -0400 (Tue, 12 Jun 2012)
New Revision: 13039

Modified:
mlpack/trunk/doc/tutorials/emst/emst.txt
Log:
A couple of formatting fixes.

Modified: mlpack/trunk/doc/tutorials/emst/emst.txt
===================================================================
--- mlpack/trunk/doc/tutorials/emst/emst.txt	2012-06-12 20:59:41 UTC (rev 13038)
+++ mlpack/trunk/doc/tutorials/emst/emst.txt	2012-06-13 00:24:27 UTC (rev 13039)
@@ -9,16 +9,16 @@
@section intro_emsttut Introduction

The Euclidean Minimum Spanning Tree problem is widely used in machine learning
-and data mining applications.  Given a set \f$S\f$ of points in \f$\mathbb{R}^d\f$,
+and data mining applications.  Given a set \f$S\f$ of points in \f$\mathbf{R}^d\f$,
our task is to compute lowest weight spanning tree in the complete graph on \f$S\f$
with edge weights given by the Euclidean distance between points.

Among other applications, the EMST can be used to compute hierarchical clusterings
-of data.  A \emph{single-linkage clustering} can be obtained from the EMST by deleting
-all edges longer than a given cluster length.  This technique is also referred to as a \emph{Friends-of-Friends} clustering in the astronomy literature.
+of data.  A <em>single-linkage clustering</em> can be obtained from the EMST by deleting
+all edges longer than a given cluster length.  This technique is also referred to as a <em>Friends-of-Friends</em> clustering in the astronomy literature.

-MLPACK includes an implementation of \emph{Dual-Tree Boruvka} on \f$kd\f$-trees, the empirically and
-theoretically fastest EMST algorithm.  For more details, see March, \emph{et al.}, \emph{Euclidean Minimum Spanning Tree: Algorithm, Analysis, and Applications}, in KDD, 2010.  An implementation on cover trees is forthcoming.
+MLPACK includes an implementation of <b>Dual-Tree Boruvka</b> on \f$kd\f$-trees, the empirically and
+theoretically fastest EMST algorithm.  For more details, see March, <em>et al.</em>, <em>Euclidean Minimum Spanning Tree: Algorithm, Analysis, and Applications</em>, in KDD, 2010.  An implementation using cover trees is forthcoming.

\b mlpack provides: